Apple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-17</td>\n",
       "      <td>222.149994</td>\n",
       "      <td>222.949997</td>\n",
       "      <td>217.270004</td>\n",
       "      <td>217.880005</td>\n",
       "      <td>217.880005</td>\n",
       "      <td>37195100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-18</td>\n",
       "      <td>217.789993</td>\n",
       "      <td>221.850006</td>\n",
       "      <td>217.119995</td>\n",
       "      <td>218.240005</td>\n",
       "      <td>218.240005</td>\n",
       "      <td>31571700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>218.500000</td>\n",
       "      <td>219.619995</td>\n",
       "      <td>215.300003</td>\n",
       "      <td>218.369995</td>\n",
       "      <td>218.369995</td>\n",
       "      <td>27123800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>220.240005</td>\n",
       "      <td>222.279999</td>\n",
       "      <td>219.149994</td>\n",
       "      <td>220.029999</td>\n",
       "      <td>220.029999</td>\n",
       "      <td>26608800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>220.779999</td>\n",
       "      <td>221.360001</td>\n",
       "      <td>217.289993</td>\n",
       "      <td>217.660004</td>\n",
       "      <td>217.660004</td>\n",
       "      <td>96246700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-09-17  222.149994  222.949997  217.270004  217.880005  217.880005   \n",
       "1  2018-09-18  217.789993  221.850006  217.119995  218.240005  218.240005   \n",
       "2  2018-09-19  218.500000  219.619995  215.300003  218.369995  218.369995   \n",
       "3  2018-09-20  220.240005  222.279999  219.149994  220.029999  220.029999   \n",
       "4  2018-09-21  220.779999  221.360001  217.289993  217.660004  217.660004   \n",
       "\n",
       "     Volume  \n",
       "0  37195100  \n",
       "1  31571700  \n",
       "2  27123800  \n",
       "3  26608800  \n",
       "4  96246700  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple = pd.read_csv('AAPL.csv')\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns]\n",
       "Open                float64\n",
       "High                float64\n",
       "Low                 float64\n",
       "Close               float64\n",
       "Adj Close           float64\n",
       "Volume                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " apple.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to_datetime can transport object into time\n",
    "apple['Date'] = pd.to_datetime(apple['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>222.149994</td>\n",
       "      <td>222.949997</td>\n",
       "      <td>217.270004</td>\n",
       "      <td>217.880005</td>\n",
       "      <td>217.880005</td>\n",
       "      <td>37195100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>217.789993</td>\n",
       "      <td>221.850006</td>\n",
       "      <td>217.119995</td>\n",
       "      <td>218.240005</td>\n",
       "      <td>218.240005</td>\n",
       "      <td>31571700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>218.500000</td>\n",
       "      <td>219.619995</td>\n",
       "      <td>215.300003</td>\n",
       "      <td>218.369995</td>\n",
       "      <td>218.369995</td>\n",
       "      <td>27123800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-20</th>\n",
       "      <td>220.240005</td>\n",
       "      <td>222.279999</td>\n",
       "      <td>219.149994</td>\n",
       "      <td>220.029999</td>\n",
       "      <td>220.029999</td>\n",
       "      <td>26608800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-21</th>\n",
       "      <td>220.779999</td>\n",
       "      <td>221.360001</td>\n",
       "      <td>217.289993</td>\n",
       "      <td>217.660004</td>\n",
       "      <td>217.660004</td>\n",
       "      <td>96246700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-09-17  222.149994  222.949997  217.270004  217.880005  217.880005   \n",
       "2018-09-18  217.789993  221.850006  217.119995  218.240005  218.240005   \n",
       "2018-09-19  218.500000  219.619995  215.300003  218.369995  218.369995   \n",
       "2018-09-20  220.240005  222.279999  219.149994  220.029999  220.029999   \n",
       "2018-09-21  220.779999  221.360001  217.289993  217.660004  217.660004   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2018-09-17  37195100  \n",
       "2018-09-18  31571700  \n",
       "2018-09-19  27123800  \n",
       "2018-09-20  26608800  \n",
       "2018-09-21  96246700  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.set_index('Date').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4e69649b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple['Adj Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc4e6516c18>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vBqC0up7S6joft8ZYQDDGZXtxOfOeX8WgxD78/bqJhAT3nl+PockxjOofy5t+lNuoqraBl3P30jeycaB7/5EqH7fI9J5PvDGdcLiilpueXklYSBBPffsMYiN632ycS8ensW7vUXYfqvB1UwBYsG4/pdX13HZu49ajFhB8zwKCCXg19Q3Me24Vhceq+efcnB6zErm9Lh6XBsACPxhcVlWe/WI3w1NiuMyVF2r/UQsIvmYBwQQ0VeW+1zbw5a7DPHDVOE4f2HtzLKbHRXJGVjwL1h3w+X7La/YeZdOBUq4/ayBJ0eGEhwSx70hl2yeaLmUBwQS0hz/exutr9nP3+cO4xPUNujebMz6dgqJythws82k7nvtiN9HhIVw+IR0RIT0u0q4Q/IAFBBOw3lp3gAcW5XPFhHRunznU183pFheNSSU4SHy6JuFQeQ3vrC/kyonpRLu2H02Pj2SfjSH4nAUEE5BW7znC3a+s44yseH53Zc9IZ+0NCdHhTMtO5C0fdhu9lLuX2gYnc88aePxYRnykDSr7AQsIJuDsPVzJLc/m0r9vBP+cm0N4SLCvm9St5oxLY//RKlbvOdLtz93gVOYv38NZgxMYmhxz/HhGfBSHKmothYWPWUAwAaW0uo6bn1lJbb2Tp759Bv36hPm6Sd3ugtGphIcE+aTb6JMtRew/WsUNblcH0DjgDY270hnfsYBgAkZZdR23zV/NjuIKHr3+dIYkRfu6ST4RHR7C10am8M76Quobune/5WeX7yYlNpzzR52YHyojvjEg7LVuI5+ygGB6PVXl7fUH+NqDS1i2rYTfXn4aZw9N9HWzfOqScWkcqqjls+2Huu05d5ZU8Gl+MddOGnjKKvB0V0CwcQTfanM/BGN6sp0lFfzizY0sLShhdFosj15/OhMG9N61Bp6aMTyJmIgQFqw9wDnDumdr2vnLdxMSJFwzKfOUx5JjIggNFptp5GMWEEyvVF3XwCOLt/PIku2EBwdx/yWjmHtWVo/b8ayrRIQGM3t0Ku9uPMhv6sYQEdq1A+tNeYtmj0kluZmNhoKDhP59bS2Cr1lAML3OkvxifvHmRnYfqmTOuDR+9vWRzf4RCnSXjk/nlVX7+GRLERee1r/tEzqhKW/R3MkDWyyTER9pq5V9zAKC6TUOHqvml29vYuGGgwxO7MPzN5/J1OzAHitozVlDEkiMDmfBugNdGhDc8xZNGtSvxXLpcZEsyS/usnaYtrU5qCwimSLyiYjkicgmEbnTdfxPIrJFRNaLyBsiEuc6niUiVSKy1vXzaAv19hORRSJS4PrXOnZNh9Q3OHli6Q7Oe2AxH+UVcff5w3j3rmkWDNrQuN9yfz7aUkRZF+5F0JS3aO5ZA1tdAJgRH0VRWQ3VdQ1d1hbTOk9mGdUDd6vqSGAycJuIjAIWAWNUdSyQD9znds52VR3v+pnXQr33Ah+pajbwkeu+Me2yavdhLv7bMn79Th6TBvVj0Q/O4Y7zsgNusVlHzRmfRm29k/c3ObrsOZryFjVlNW1J00yjwmP+tatbIGkzIKhqoaqudt0uA/KAdFX9QFWblhUuBzLa+dyXAs+4bj8DXNbO800AO1JRyz2vrufKR77gWFUdj14/kae+fQYDEnpn6uquMiEzjsx+kSzooo1zmstb1JKmtQg2juA77RpDEJEsYAKw4qSHbgJecrs/SETWAKXAz1R1aTPVpahqITQGHRFJbk9bTOBaWlDM919cQ2l1PbdMH8yd52XTp40/NqZ5TfstP7pkByXlNSRGh3u1/ubyFrWkabWyrUXwHY8XpolINPAacJeqlrod/ymN3UrzXYcKgQGqOgH4IfCCiMR2tIEicouI5IpIbnGxDTgZeHBRPtERIbzz/an85KKRFgw6ac64dBqcysIN3t1vuaW8RS3p3zeC4CCxqac+5FFAEJFQGoPBfFV93e34jcDFwHXqSp2oqjWqesh1exWwHRjWTLUOEenvqqc/UNTcc6vqY6qao6o5SUnds4DG+K8jFbWs3XuUyydkMCK1w98zjJvhqTEMT4nx+k5qLeUtaklIcBCpsRG2OM2HPJllJMCTQJ6qPuh2fDZwDzBHVSvdjieJSLDr9mAgG9jRTNULgBtdt28E3uzoizCB49OCYlQbV9oa75kzPo3c3Ue82n/fUt6i1qTHWRpsX/LkCmEKMBeY6TaV9CLgYSAGWHTS9NLpwHoRWQe8CsxT1cMAIvKEiOS4yv0eOF9ECoDzXfeNadWSrcXER4UyLiPO103pVea4dot7a513uo1ay1vUGluc5lttdr6q6jKgucnDC1so/xqN3UvNPfZdt9uHgPM8a6Yx4HQqS/KLmZadZCkovCyzXxQTB8SxYN0BvjdjSKfre76VvEWtSY+P5GBpNXUNTkLbEUiMd9g7bnqMjQeOcaii1rqLusiccWnkFZZS4OjcfstVtQ280kreotZkxEfi1MZV56b7WUAwPcbirY2zzKZ3U3bOQPP1sWkECZ3eOKcpb9ENZ2W1+9z0uMZ1JDaw7BsWEEyPsXhrEWMz+np9rrxplBQTzvRhSfx98TZufOpLPth0sN0b6LjnLTojq/3ZaGxxmm9ZQDA9wtHKxummM+zqoEs9+M3xfH9mNlsOlnLLc6uY+odPeHBRvsdbW3qat6gl/eMau5hsLYJv2Ioe0yN8WlCCU+Gc4bagvSv16xPGD84fxh0zh/LxliJe+HIPf/u4gIc/LuDc4clce+YAZgxPbnFQvylv0eVt5C1qSXhIMCmx4dZl5CMWEEyPsHhrEXFRoYzPtOmm3SEkOIgLRqdywehU9h6u5KWVe3kpdy8fPZNLWt8Irj5jAFefkUlq368GjUtceYuumZTZqdXjthbBd6zLyPg9p1P51Kab+kxmvyh+NGs4n987k0evn8iQ5Gge+jCfKX/4mP96NpdPthbR4FRebkfeotZkxEex76iNIfiCXSEYv7fpQCkl5bU2fuBjocFBzB7Tn9lj+rP7UAUvfrmXV1ftZdFmB+lxkVTXNXD2EM/yFrUmPT6ShRsKaXCqfQHoZnaFYPze4q2Naa5suqn/GJjQh3svHMHn957Hw9dOYGBCFIcra/nutEGdrjsjPpJ6p+IotbUI3c2uEIzfW5xfzGnpfUmKsemm/iYsJIiLx6Zx8dg0qusaiAjt/MZEx9NgH60izXXbdA+7QjB+7WhlLWv2HLHVyT2AN4IBNI4hgK1F8AULCMavLXVNN7WAEDhsoxzfsYBg/NrircX0jQxlfGb7V72anikyLJjE6DBbnOYDFhCM3/oqu2mizTYJMOlxkbY4zQcsIBi/tbmwlJLyGmbY6uSAkx5vi9N8wQKC8VtN003PsemmAadxcVoVTqf6uikBxQKC8VuLtxYzJj3WppsGoPS4SGrrnZRU1Pi6KQHFAoLxS8cq61i95wgzhll3USD6Kg22dRt1JwsIxi8t3VZs000DWHq8TT31hTYDgohkisgnIpInIptE5E7X8T+JyBYRWS8ib4hInOv4+SKySkQ2uP6d2UK994vIfhFZ6/q5yLsvzfRki7cWExsRYtlNA1TTWgS7Quhenlwh1AN3q+pIYDJwm4iMAhYBY1R1LJAP3OcqXwJcoqqnATcCz7VS90OqOt71s7DDr8L0Ksenmw5LIsQ2Wg9IMRGh9I0MZb9lPe1Wbf62qWqhqq523S4D8oB0Vf1AVetdxZYDGa4ya1S1aVPWTUCEiNiooPHY5sJSistqLLtpgMuIt7UI3a1dX79EJAuYAKw46aGbgHebOeVKYI2qtjRV4HZXl9NTImJLUQ0AS/KLATjHxg8Cmm2U0/08DggiEg28BtylqqVux39KY7fS/JPKjwb+ANzaQpWPAEOA8UAh8EALz3uLiOSKSG5xcbGnzTU92OKtRYxOiyU5JqLtwqbXyoiPYt+RKlRtLUJ38SggiEgojcFgvqq+7nb8RuBi4Dp1+18TkQzgDeAGVd3eXJ2q6lDVBlV1Ao8Dk1oo95iq5qhqTlKSfWPs7Y5V1bF6z1GbXWRIj4+kqq6BI5V1vm5KwPBklpEATwJ5qvqg2/HZwD3AHFWtdDseB7wD3Keqn7VSb3+3u5cDG9vffONrH2528MTSHV6rb1lBCQ1OtXQVxm0tgg0sdxdPrhCmAHOBmSdNEX0YiAEWuY496ip/OzAU+Llb+WQAEXlCRHJc5f7ompq6HjgX+IE3X5jpeh9udnDr86v49Tt5vL56n1fqXLy1iNiIECbYdNOA549psEur67j9hdUUHvOfNnlTmzumqeoyoLlUk81OE1XVXwO/buGx77rdnuthG40f+nLnYW57YTWj02IJDwni5//ZyIQB8QxK7NPhOlWbspvadFMDmcc3yvGfP77Ltx/i7fWFjEnvy7xzhvi6OV5nv3Wm3TYdOMbNT68kPT6Sp78zib98awIhwUF8/8U11NY7O1zv5sJSispqbHaRASA2MoTo8BC/2hehoKgcgKUFvXOCiwUE0y47Syq48akviYkI4fmbz6RfnzDS4iL50zfGsmH/Mf743pYO1714a+Mvma0/MAAi4ndrEfIdZQCs3HmEqtoGH7fG+ywgGI85SquZ++QKnArP3nzmCRugXzA6lRvOGsgTy3byiSttdXst2VrMqP6xJMfadFPTqHGjHP8ZVM53lBMTHkJtg5Mvdx32dXO8zgKC8cjRylpuePJLjlTU8vR3zmBocvQpZX5y0UhGpMbwo5fXUVRa3a76j1XVsWrPEZtuak6QHh/pN11G9Q1OtheXc9mEdMKCg1ia3/u6jSwgmDZV1tZz09Mr2VlSweM35DA2o/kZQBGhwTx87QQqauv5wctr27W5yWfbbLqpOVVGfCRl1fUcq/L9WoTdhyuprXcyLjOOMwbFs7SgxNdN8joLCKZVtfVO5j2/mrV7j/LXa8Zz9tDEVssPTY7h/ktG89m2QzyypNk1ic1avLWImIgQJg6w6abmK+lxjTON/GHqaYFr/GBYSjRThyax1VHW7ithf2cBwbSowan88OW1fJpfzO+uOI3ZY/q3fRJw9RmZfH1sfx5clM/qPUfaLN803XS6TTc1J/GnxWn5jsYZRkOTo5mW3fjFqLddJdhvn2mWqnL/gk28vb6Q+y4cwdVnDPD4XBHhd1ecRv++EXz/xTVtXu7nFZbhKLXppuZUxzfK8YNxhHxHGZn9IokKC2FU/1gS+oSxbJsFBBMAHvqwgOeW7+bWcwZzawcW4MRGhPLXayZQeKyan7yxodUEZYvzG2cl2XRTc7KEPmFEhAb5xdTTAkc5w5JjAAgKEqYMTWRpQUm7xsr8nQUEc4p/fbaTv35UwNU5mdw7e0SH65k4IJ67LxjGO+sLeWnl3hbLLbbppqYFIuIXabDrGpzsKCknOyXm+LFp2YmUlNew5WCZD1vmXRYQzAneWLOP/31rM7NGp/Cby8fQmNuw4+ZNH8LUoYnc/9am44Ny7kqr61i126abmpZlxEexz8c7p+0qqaCuQRme+tV062nZjZ/ZZdt6z/RTCwjmuI+3OPjRK+s5e0jC8XQUnRUUJDz4zXH0CQvhjhfXUF134urOzyy7qWlDerzvrxCaBpSzk7+6QkjtG0F2cnSvGli2gGCAxmR133u+MVndYzfkEBEa7LW6k2Mj+L9vjmPLwTJ+807eCY8t3lps001NqzLiIzlSWUdFTX3bhbtIvqOMIOGUBZnTspNYsfPwKV90eioLCIaS8hpufqYxWd2/vn0G0eFtJsFtt3OHJ/PdqYN4bvlu3tt4EHDPbppo001Ni46nwfbhTKOCojIG9Is65YvStOxEauudrOwlaSzst9Dw0sq9lFXX8+j1p5MQHd5lz/Pfs0dwWnpf7nltPQeOVrHlYBkHS6uZMcy6i0zLMo6nwfbdOEK+48QB5SZnDu5HaLD0mm4jCwgBrsGpvLBiD2cPSWBYMx94bwoLCeKv10ygvsHJXf9ey8dbGqeb2voD05qmxWm+GkeoqW9gV0kFw5v5/YgKCyFnYD8LCKZ3WLy1iP1Hq7h+8sBueb5BiX341WVj+HLXYf7yUQEj+8eSYtNNTSuSosMJC/bdWoSdJRXUO5XslFMTOgJMzU4kr7CUorKen8bCAkKAe275bpJjwjl/VEq3PecVEzO4YkI6tfVOm27cgzI3AAAXOklEQVRq2hQUJKTFRbDPR2MITTOMWrqCnu6afvpZL1i1bAEhgO05VMmS/GKumTSA0G4e1P3lZWOYO3kg17QjJYYJXBnxUT7rMipwlBEcJAxOan572NFpscRHhfaKbqM2/wqISKaIfCIieSKySUTudB3/k4hsEZH1IvKGiMS5nXOfiGwTka0iMquFegeJyAoRKRCRl0QkzHsvy3hi/pe7CRLhmknd/0c5OjyEX102hgEJUd3+3Kbnadwox1dXCGUMTIgiPKT5qdjuaSxaS9HSE3jytbAeuFtVRwKTgdtEZBSwCBijqmOBfOA+ANdj3wJGA7OBf4hIc+/kH4CHVDUbOALc3NkXYzxXXdfAyyv3cv7IFFL7Wh++8W/p8ZGUlNf4ZL6/ew6jlkzPTqK4rIatzazG70naDAiqWqiqq123y4A8IF1VP1DVppUiy4EM1+1LgX+rao2q7gS2AZPc65TGfAgzgVddh54BLuvsi+kNVJWPtzi6fBHOwg2FHKmsY+5Z3TOYbExnZPgo62l1XQO7DlUwLLX1gDDVlQ57WQ/vNmpXx7GIZAETgBUnPXQT8K7rdjrgnslsn+uYuwTgqFtAaa5MQHpy2U5uejqXX7+zuUuf5/nluxmc2IezhyR06fMY4w3HF6d1c7fR9uJynNq4KU5r0uIiGZLUh08DJSCISDTwGnCXqpa6Hf8pjd1K85sONXP6yR1rnpRpqv8WEckVkdzi4t6TRKo5K3Yc4nfvbiE2IoSXVu5tNhmcN2zcf4zVe45y3eSBnU5eZ0x3yOjXtDitewNCQRszjNxNy07iy52HenQaC48CgoiE0hgM5qvq627HbwQuBq7Tr0ZT9gGZbqdnAAdOqrIEiBORkFbKAKCqj6lqjqrmJCX13imKjtJqbnthDQP7RfH2HdPoExbCH97b0iXPNX/FbiJCg/jGxIy2CxvjB1JiwgkOEvZ3c9bTfEcZIUFCVkLzM4zcTctOpLrOyardbe8S6K88mWUkwJNAnqo+6HZ8NnAPMEdV3f+XFgDfEpFwERkEZANfutfpCh6fAN9wHboReLMzL6Qnq2twctv81VTU1PPo3NMZkBDFvBlD+DCviBU7Dnn1uUqr6/jPmgPMGZdG36hQr9ZtTFcJCQ6if9+Ibr9CyHeUMyixD2EhbX93njw4gdBg4dOCntuT4ckVwhRgLjBTRNa6fi4CHgZigEWuY48CqOom4GVgM/AecJuqNgCIyEIRSXPVew/wQxHZRuOYwpPefGE9ye8WbiF39xH+8I2xxy9Nb5oyiNTYCH737havTmV7fdU+quoamDs5y2t1GtMdfLFRTkFRmccpXfqEhzBxQLzXB5ZVlbfXH6C+wenVepvjySyjZaoqqjpWVce7fhaq6lBVzXQ7Ns/tnN+o6hBVHa6q77odv0hVD7hu71DVSa56rlLVmq55if7trXUHeOqznXz77CzmjEs7fjwyLJgfnj+MtXuP8q4rO2hnqSrPr9jDuMw4Tsvo65U6jekuGfFR3XqFUFXbwJ7Dle3K8TUtO5FNB0opKffen7M31x7g9hfWeO3vQGtspbIPFTjKuOe19eQMjOcnF4085fErT89gWEo0f3xvC3Ve+HawfMdhthWVc/2ZtjrY9Dzp8ZE4yqqpre/6b8oA24rKUQ9mGLmb5uU0FhU19fzu3TzGZvTl66f190qdrbGA4CNl1XXc+vwqosJC+Pt1E5vtowwOEu67cCS7DlXy4pd7Ov2czy/fTd/IUC5xuxIxpqfIiI9EFQqPdc9VQr5rll9zaa9bMia9L3FeTGPx90+24Sit4X8uGU1QUNfPCLSA4AOqyn+/up7dhyp5+NoJrWb7nDE8icmD+/GXDwsoq67r8HMWlVbz/qaDfDMnw6u7oRnTXTK6eS1CflEZYcFBZLUjvUpwkDBlSCJLC4o7Pfa3q6SCJ5bu5IoJ6Zw+ML5TdXnKAoIPPLF0J+9uPMi9s0cweXDrC8NEGq8SDlXU8vinOzr8nP9euZd6p3LtmbYy2fRMX22U0z0BocBRzuCkPu3ezW9adiKO0hq2FZV36vl//U4eocHCPReO6FQ97WEBoZst33GI37+3hYtOS+W70wZ5dM64zDguHtufx5fupKi0/TnX6xucvLBiD9OyExmU2PZ8amP8UWrfCETotjTY+Q7PZxi5a0pj0ZlVy4u3FvFhnoPbZ2Z3634hFhC6kaO0mttfWMPAhCj++I1x7Vol/ONZw6l3Onnow4J2P++HeUUcLK3utk1wjOkKYSFBpMZGdMtWmhU19ew7UtWuAeUmGfFRDE7sw9IOrkeorXfyy7c3k5UQxU1TszpUR0dZQOgmTYvPKmvr+ef1p7d7I/uBCX247syBvLRyD9uK2pfSYv6K3fTvG8F5I2zvYtOzdddahAJXd097BpTdTctOZMWOw9TUtz+NxbNf7GJHcQW/uGRUiym3u4oFhG7y24V5jYvPrhzb4Q/ZHTOHulJabPX4nB3F5SwtKOHaSQPa3RdqjL9Jj4/sloynTTOMOrrP+LTsJKrqGtqdxqKorJo/f1jAjOFJzBzRfbsYNrG/EN1gwboD/OuzXdw0ZVCnpnwmRIczb8YQFm12sHLXYY/Omb9iDyFBwtWTMtsubIyfy4iPpPBYdZev2i1wlBEeEsSAfh3bwGnykARCgqTd00//9N5Wauob+PnFozr0vJ1lAaGL5TvKuOfV9ZyRFc99F3V+tsBNUwaREhvObxfmtTmtraq2gVdX7WPWmFSSY2wTHNPzpcdF0eBUHGVdm9gg31HOkKRogjs49z+6A2ks1u49yiur9nHTlEEMSWr/2IU3WEDoQmXVdcx7bhXRESH8/dqJXtm3uCmlxZo9R3mvjaXsb60/wLGqOubaYLLpJZo2ytl3uGsHlgscZQxvY1OctkzNTmTjgWMcrqhts6zTqdy/YBOJ0eHcPnNop563MywgdBFV5cevrGf34Ur+fu1Ekr04dezKia6UFu9vbTWlxfPLd5OdHM2Zg/p57bmN8aX0btg5ray6jgPHqsnuwAwjd9OyE1H1LI3F62v2s3bvUe69cAQxEb7LQty+qS7mBBU19RSV1eAorcZRWk1RaQ1FZdU4SmvYc7iStXuP8rOvj2SSl/8ghwQHce+FI7jp6Vz+/eUe5p6VdUqZdXuPsn7fMf53zmjbBMf0Gk07p3Xl4rT8pk1x2thHuS1jM+KIjQhhaUFxq2OHZdV1/OG9LYzLjOOKCb7dONICQiuOVtayfMdh9h2pPP6Hv6i0BkdZ47/lzex7HBHaOFc6OTaCO8/L5uapni0+a69zhydz5qB+/OWjAi6fmHHKNNbnl+8mKiyYyyfazqSm94gIDSYxOrxLp54WdHKGUZPgIGHK0ESWFpSgqi1+MXv4420Ul9Xw+A053ZKvqDUWENzUNzhZt+8YS/KL+TS/mPX7juJ0jduGhwSREhtBSmw4I/vHcs6w8OP3U2IaA0BybDgx4SHd8o1cRLjvopFc9vfPeOzTHfzw/GHHHztaWcuCdQe4YmIGsT68/DSmK2TER7KvC3dOy3eUExkafHy8ojOmZSfx7saDbC+uYGjyqV1QO4rLeeqznVx1egbjM+M6/XydFfABofBYFZ/mF7Mkv5hlBSWUVtcTJI3pIu6Ymc30YYkMTYohNrJ7/tC3x/jMOL4+tj+Pf7qD688ccHyc4tVV+6ipd3L9ZEtzbXqf9PhINu0/1mX1FxSVMTQ52ivf1qe50lgsLShuNiD86u3NhIcE8+PZwzv9XN4QcAGhuq6BL3cePn4V0LQiMTU2gtljUjlnWDJThiYQFxXm45Z65r9nDeeDTQf580cF/Pby03A6lfkr9nD6wHhGp9kmOKb3yYiPZNEmB06ndkkXS76jjKlDvbN/e2a/KLISolhaUMJ3ppzYffzxFgefbC3mpxeN9Jtp4QEREHaWVPDxliKW5BezYschauqdhIUEceagfnwzJ5Ppw5IYlhLtd1cAnmhKafHc8t3cNGUQhceq2FlSwffP893UNWO6UkZcJLUNTorLa7ye+O1YZR2O0poO5TBqybTsJF5bvY9a198dgJr6Bn71dh6Dk/pw49lZXnuuzgqIgPD40h28sGIPg5P6cO2ZA5g+LInJgxKIDOsd+wLcMXMor67axx/f24II9OsTxoVjun53JWN84as02JVeDwj5Rd4ZUHY3LTuR55bvZvWeI8fT3f/rs13sLKng6e+c0ezmWL7SZkAQkUzgWSAVcAKPqepfROQq4H5gJDBJVXNd5a8DfuxWxVhgoqquPane+4H/AppSAv5EVRd26tW04HvnDOH/zRhy/IPU2yREhzPvnMH83wf5iMCt04fYJjim12pai7DvSBWne3nN5Ve7pHnvCmHykASCg4RlBSVMHpxAUWk1f/uogK+NTGbGcP9KOOlJaKoH7lbVkcBk4DYRGQVsBK4APnUvrKrzVXW8qo4H5gK7Tg4Gbh5qKttVwQAa+/F6azBocvPUwaTEhgNwne2ZbHqxrlyLUOAop09Y8PHn8IbYiFAmZMYdT4f9+/e2UNeg/OzrvslX1Jo2rxBUtRAodN0uE5E8IF1VFwFt9btfA7zohXaaNkSGBfPAVeMpKCojs4MJuYzpCfqEhxAfFdolq5XzHWVkp8R4fTxxWnYSf/4on4+3OHh99X6+N2MIWX64WVW7Oq9EJAuYAKzw8JSraT0g3C4i60XkKRHpnk1De7Gp2YmnzGQwpjfKiI/qkiuEfEe5VweUm0x1pbG444U1pMSGc/u5/jnpw+OAICLRwGvAXapa6kH5M4FKVd3YQpFHgCHAeBqvQB5ooZ5bRCRXRHKLizu2A5Expndp3CjHu4vTDlfUUlJe49UB5SbjMvoSExFCRW0D9144gj7t3CCru3gUEEQklMZgMF9VX/ew7m/RytWBqjpUtUFVncDjwKQWyj2mqjmqmpOU5J25wcaYnq1po5y2UsC3x1cDyt4PCCHBQcwZl8a07EQuG++/6WQ8mWUkwJNAnqo+6EmlIhIEXAVMb6VMf9f4BMDlNA5SG2NMmzLiI6muc3KoopbE6HCv1PlVDqOu2YvgN5ef1iX1epMnVwhTaJwtNFNE1rp+LhKRy0VkH3AW8I6IvO92znRgn6rucK9IRJ4QkRzX3T+KyAYRWQ+cC/yg8y/HGBMImmYBeTPJXb6jnJjwEFK9vLahJ/FkltEyoKUh9zdaOGcxjVNUTz7+Xbfbcz1rojHGnOirxWlVjPNSUrh8RxnDUr0/w6gn8Z8lcsYY46GvNsrx3sByQVHXzDDqSSwgGGN6nL6RocREhHht6mlJeQ2HK2rJ7uSmOD2dBQRjTI/UOPXUOwEh/6D3cxj1RBYQjDE9kjcXp+V38QyjnsICgjGmR8rw4lqE/KJy+kaGkhTjnSmsPZUFBGNMj5QRH0l5TT3Hquo6XVeBo4zhXZDDqKexgGCM6ZG8lfVUVcl3lHs15XVPZQHBGNMjDUpqzBb6yZaiTtVTXFbDsaq6gB9QBgsIxpgeanhKDBedlspfPy5gy8E28222aGsXbIrTU1lAMMb0SCLCry4dQ2xEKHe/vI66BmeH6sl3lAM25RQsIBhjerCE6HB+e8VpbDpQysMfb+tQHQWOMvr1CfNakryezAKCMaZHmzU6lcsnpPP3T7axcf+xdp+f7ygL+PUHTSwgGGN6vPsvGU1CdBg/fHktNfUNHp+nqhQ4yq27yMUCgjGmx+sbFcrvrxxLvqOchxYVeHxe4bFqymrqu2RTnJ7IAoIxplc4d3gyV+dk8tin21m954hH5xxPWZFsXUZgAcEY04v87OKR9O8byY9eXkdVbdtdRwU2w+gEFhCMMb1GTEQof/zGWHaUVPCn97e2WT7fUUZSTDjxfcK6oXX+zwKCMaZXmTI0kRvOGsi/Pt/Jih2HWi2bb5vinMACgjGm17n3whEM6BfFj15dR0VNfbNlnE5lm6Ms4DfFcddmQBCRTBH5RETyRGSTiNzpOn6V675TRHLcymeJSJWIrHX9PNpCvf1EZJGIFLj+jffeyzLGBLKosBD+76px7DtSxW8X5jVbZv/RKipqG2z8wI0nVwj1wN2qOhKYDNwmIqOAjcAVwKfNnLNdVce7fua1UO+9wEeqmg185LpvjDFecUZWP26eMoj5K/awtKD4lMcLimxTnJO1GRBUtVBVV7tulwF5QLqq5qlq26M2LbsUeMZ1+xngsk7UZYwxp/jRrOEMSerDf7+6ntLqE/dNaMphZGsQvtKuMQQRyQImACvaKDpIRNaIyBIRmdZCmRRVLYTGoAMkt6ctxhjTlojQYP7vqnE4Sqv51VubT3gs31FGamwEfSNDfdQ6/+NxQBCRaOA14C5VbS3XbCEwQFUnAD8EXhCR2I42UERuEZFcEcktLj71ss8YY1ozYUA835sxhFdW7eOjPMfx4wW2Kc4pPAoIIhJKYzCYr6qvt1ZWVWtU9ZDr9ipgOzCsmaIOEenvqr8/0OwuF6r6mKrmqGpOUlKSJ801xpgTfP+8bEakxnDv6xs4UlGL06kUFJXZgPJJPJllJMCTQJ6qPuhB+SQRCXbdHgxkAzuaKboAuNF1+0bgTU8bbYwx7REeEswD3xzHkYpa/mfBJvYeqaS6zmkDyicJ8aDMFGAusEFE1rqO/QQIB/4GJAHviMhaVZ0FTAd+KSL1QAMwT1UPA4jIE8CjqpoL/B54WURuBvYAV3nxdRljzAlGp/XljpnZPPRhPqHBjd+FbUD5RKKqvm6Dx3JycjQ3N9fXzTDG9FB1DU6u+MfnbHDtm7Dh/guIiej9g8oiskpVc9oqZyuVjTEBIzQ4iAe+OY6w4CDS4yIDIhi0hyddRsYY02sMS4nhwavHUV3XsT2YezMLCMaYgHPx2DRfN8EvWZeRMcYYwAKCMcYYFwsIxhhjAAsIxhhjXCwgGGOMASwgGGOMcbGAYIwxBrCAYIwxxqVH5TISkWJgdwdPTwRKvNic3sjeo9bZ+9M2e49a56v3Z6Cqtrl/QI8KCJ0hIrmeJHcKZPYetc7en7bZe9Q6f39/rMvIGGMMYAHBGGOMSyAFhMd83YAewN6j1tn70zZ7j1rn1+9PwIwhGGOMaV0gXSEYY4xpRUAEBBGZLSJbRWSbiNzr6/b4GxHZJSIbRGStiNgepYCIPCUiRSKy0e1YPxFZJCIFrn/jfdlGX2rh/blfRPa7PkdrReQiX7bRl0QkU0Q+EZE8EdkkIne6jvv1Z6jXBwQRCQb+DlwIjAKuEZFRvm2VXzpXVcf785S4bvY0MPukY/cCH6lqNvCR636geppT3x+Ah1yfo/GqurCb2+RP6oG7VXUkMBm4zfV3x68/Q70+IACTgG2qukNVa4F/A5f6uE3Gz6nqp8Dhkw5fCjzjuv0McFm3NsqPtPD+GBdVLVTV1a7bZUAekI6ff4YCISCkA3vd7u9zHTNfUeADEVklIrf4ujF+LEVVC6HxFx5I9nF7/NHtIrLe1aXkV90hviIiWcAEYAV+/hkKhIAgzRyzqVUnmqKqE2nsVrtNRKb7ukGmR3oEGAKMBwqBB3zbHN8TkWjgNeAuVS31dXvaEggBYR+Q6XY/Azjgo7b4JVU94Pq3CHiDxm42cyqHiPQHcP1b5OP2+BVVdahqg6o6gccJ8M+RiITSGAzmq+rrrsN+/RkKhICwEsgWkUEiEgZ8C1jg4zb5DRHpIyIxTbeBC4CNrZ8VsBYAN7pu3wi86cO2+J2mP3QulxPAnyMREeBJIE9VH3R7yK8/QwGxMM01/e3PQDDwlKr+xsdN8hsiMpjGqwKAEOAFe39ARF4EZtCYndIB/A/wH+BlYACwB7hKVQNyYLWF92cGjd1FCuwCbm3qLw80IjIVWApsAJyuwz+hcRzBbz9DAREQjDHGtC0QuoyMMcZ4wAKCMcYYwAKCMcYYFwsIxhhjAAsIxhhjXCwgGGOMASwgGGOMcbGAYIwxBoD/DxWLFeUR6GpPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(apple['Adj Close'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}